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Pedestrian Simulation Taking into Account Stochastic Route Choice and Multidirectional Flow
Published in Edward Chung, André-Gilles Dumont, Transport Simulation, 2019
Miho Asano, Masao Kuwahara, Agachai Sumalee
For each route, the demand associated with it is dynamically loaded onto several paths (or trajectories) by the flow propagation model. The model performs the dynamic flow propagation and calculates the delay that occurs at each point and time. The flow propagation model is based on the cell-transmission model (CTM) that divides a continuum space into a number of cells, and a pedestrian can move in and out of each cell with the delay based on the speed-density relationship considering the interaction of multi-directional flows.
Unmanned aerial vehicle path planning for traffic estimation and detection of non-recurrent congestion
Published in Transportation Letters, 2022
Cesar N. Yahia, Shannon E. Scott, Stephen D. Boyles, Christian G. Claudel
The traffic state is represented by densities propagated forward using the cell transmission model. The incident severity is represented by the free flow speed and critical density parameters at incident prone locations. The parameters are propagated forward using a random walk. On the other hand, the parameters are updated based on corresponding updates; this parameter update procedure aims to maintain a monotonic relationship between parameters ( and ) and speed-density observations. Equivalently, we can propagate using a random walk and update based on the corresponding updates. We consider that the traffic state is directly observed using loop detector density measurements. We also consider that, for the given best estimate on traffic densities, the incident parameters are observed using less frequent speed measurements.
IMM/EKF filter based classification of real-time freeway video traffic without learning
Published in Transportation Letters, 2022
Based on (Celikoglu 2013), the author of (Celikoglu 2014) proposed a dynamic approach to specify flow pattern variations that are simulated by a multimode macroscopic model, incorporating the theory of neural networks (NNs), to reconstruct real-time traffic densities. Data are dynamically and simultaneously input to the NN, for density estimation and traffic flow modeling processes. Traffic flow is simulated by modifying the cell transmission model, which is a first order macroscopic model, in order to explicitly account for flow condition transitions, considering wave propagations. Cell-specific flow dynamics are used to determine the mode of prevailing traffic conditions, which are, in turn, sought to be reconstructed by neural methods. The classification of flow patterns from the fundamental diagram is obtained by considering the traffic density as a pattern indicator, in order to model the spatial differentiation of flow propagation wave fronts, the authors adopt the switching multimode approach presented in (Sun, Muñoz, and Horowitz 2003) and (Muñoz et al. 2003), which stands as a boundary for pattern transitions, throughout the freeway stretch. Switching procedure processes with several sets of linear difference equations, depending on the stretch boundary inputs and the flow pattern of each cell (freeway section) in the stretch. The flow pattern of a given cell is determined by comparing the current cell density with the critical density.
A congestion-aware Tabu search heuristic to solve the shared autonomous vehicle routing problem
Published in Journal of Intelligent Transportation Systems, 2021
Prashanth Venkatraman, Michael W. Levin
We demonstrate the algorithm on the well-known Sioux Falls network which consists of 24 nodes and 76 links. The Sioux Falls static traffic assignment network used appears on https://github.com/bstabler/TransportationNetworks. The 24 zones are unchanged. Cell lengths are determined by the cell transmission model, i.e. the cell length is related to free flow speed and time step by We used a time step of 6 seconds. Free flow speeds and capacities are unchanged. The trip table distributes the original static traffic assignment trips over 3 hours. At the beginning of the time horizon, the SAVs were uniformly distributed across the 24 zones in the network. The demand was based on a trip table uniformly distributed over the time horizon, and scaled proportionally.